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The self-service Business Intelligence (BI) trend that has been sweeping the corporate world isn’t going away anytime soon. Hyper-competitive industries where time-to-decision is critical have been the early adopters of self-service BI tools. The financial services, retail and healthcare/life science markets are leading the way.
But there is also functional adoption occurring in other industries as well. For instance, sales and marketing teams are turning to self-service BI to gain a quicker understanding of customer buying models, sentiment patterns, e-commerce data and client behaviors, so they can easily and rapidly adapt their plans and outreach. The technology is also used frequently in the Office of Finance. In this case, financial analysts need to be able to quickly pull planning, budgeting and forecasting information from across various departments, and then blend it all together to understand overall expenses and costs.
Quite simply, self-service BI tools add tremendous value to organizations across industries – far beyond what is required in an initial investment. The technology improves business agility and speeds time-to-insight enabling users to quickly understand issues and take action. But, many organizations are finding that they can only achieve maximum value from their self-service BI tools if they are combined with a self-service data preparation solution. Here’s why.
Data Access and Preparation Challenges
The biggest challenges users face with self-service analytics is that data is not always accessible or in an analysis-ready format. In fact, data that provides the most analytical value has traditionally been locked away in multi-structured, semi-structured and unstructured documents, such as text reports, web pages, PDFs, JSON and log files. Adding to this complexity, data is rarely analysis-ready because it comes from multiple, disparate sources, and in varying formats.
Traditionally, the only way for business users and data analysts to access and use this information has been to manually rekey and reconcile the data, which are time-intensive and error-prone processes. As a result, they spend much more time gathering and preparing data than they do analyzing it.
Self-service data preparation solutions help business users and data scientists overcome these challenges by empowering them to rapidly and easily acquire, manipulate, blend, clean and prepare data from virtually any source for analysis in tools like Excel, IBM Watson Analytics and Tableau. Data can be prepared for analysis in a fraction of the time that it takes using spreadsheets and other manually-intensive measures. And, with the ability to retrieve and use not only the right data but all of the data required to get the whole story, users can focus on performing analysis that will result in timely, more informed business decisions and better operational processes.
Self-Service Data Preparation: A Win-Win for Business Users and IT
In the self-service analytics world we live in, business users must be able to quickly and easily access the data they need to make more informed business decisions. The problem is that much of this data is housed in repositories protected by IT or within sources that require IT intervention. Because IT professionals are charged with data protection and governance, they prefer to make information available on an as-needed basis, rather than creating an environment of open access. Consequently, business users are left with no choice but to rely on IT to get the data they need for analysis, which can cause delays in decision making.
The good news is that self-service data preparation solutions bridge the gap between the ease-of-use and agility that business users demand and the automation, scalability and governance required by IT. Data preparation solutions address governance risks by securely storing, managing and controlling access to source content, prepared data, reusable extraction and prep models, and created visualizations and dashboards – without impeding self-service analytics processes. Many also offer governance capabilities such as data retention, data masking, data lineage, role-based access and auditing functions to further ease IT’s apprehensions. And, despite all of these controls, business users are still able to access the data they need when they need it with no restrictions.
A Powerful Combination
One of the most significant challenges faced by the BI market today is that companies purchase self-service analytics solutions, but don’t always fully implement them because of data retrieval and preparation challenges. Self-service data preparation fills this void found in so many BI tools and improves the likelihood that they will be employed across the company and used to their full potential. Together, self-service BI and self-service data preparation empower ordinary users to do extraordinary things with their data, and foster a culture of data-driven decision making that delivers business value.